基于核函数极限学习机和小波包变换的EEG分类方法  

EEG classification algorithm based on kernel extreme learning machine and wavelet packet transform

作　　者：王丽 兰陟 杨荣 王强 李宏亮 WANG Li;LAN Zhi;YANG Rong;WANG Qiang;LI Hongliang(National Research Center for Rehabilitation Technical Aids,Beijing 100176)

机构地区：[1]国家康复辅具研究中心,北京100176

出　　处：《北京生物医学工程》2018年第5期481-487,524,共8页Beijing Biomedical Engineering

基　　金：国家科技支撑计划(2015BAI06B00)资助

摘　　要：目的为实现运动功能障碍患者的运动意愿,基于脑-机接口(brain-computer interface,BCI)的康复训练技术是近年来的研究热点。脑-机接口的关键技术是快速准确地识别出与运动想象相关的脑电模式。针对脑电信号非平稳及个性化差异等特点,利用小波包理论和核函数极限学习机(extreme learning machine,ELM)方法,提出一种自适应的特征分类方法来提高脑电信号的分类识别率。方法由于小波包存在着频带交错的现象,所以首先利用距离准则将自适应提取的最优小波包的平均能量作为特征向量,并采用核函数ELM方法进行分类。最后利用BCI竞赛数据进行了脑电信号特征分类的仿真研究,并对不同算法的分类识别率进行仿真分析。结果自适应特征分类方法对用于实验的脑电数据的平均分类识别率达到97.6%,对比ELM、神经网络(back propagation,BP)和支持向量机(support vector machine,SVM)分类方法,核函数ELM方法在分类时间和识别精度上效果最佳。结论本文提出的脑电信号分类方法取得了较高的分类识别率,适用于脑电信号的分类应用。Objective The rehabilitation technology based on brain.computer interface （ BCI） has become a crucial issue for the patient with motor dysfunction to achieve movement. The key technique of BCI is to quickly and accurately identify the EEG mode which is associated with motor Imagery. An adaptive algorithm of classification based on wavelet packet transform and kernel extreme learning machine （ELM） algorithm is proposed according to the characteristic of EEG such as Non.stationary and individualized differences and so on to enhance the classification accuracy of EEG. Method As the existence of the cross.banding of wavelet packet,the average energy of the best wavelet packet basis which is extract adaptively using distance criterion form the feature vector, and the kernel ELM algorithm is applied for classification. BCI competition data are used for the classification of the proposed method. The classification accuracy of different algorithms is simulated andanalyzed. Results Simulation results demonstrate that the average classification accuracy is achieved to 97.6% and outperforms state.of.the.art algorithms such as ELM, back propagation （BP） and support vector machine （SVM） in the aspects of training time and classification accuracy. Conclusions The proposed method produces a high classification accuracy and is suitable for EEG classification application.

关 键 词：脑-机接口 小波包变换 核函数极限学习机 分类方法 

分 类 号：R318.04[医药卫生—生物医学工程]